Public Health

Retraction: Machine learning-based anomaly detection and prediction in commercial aircraft using autonomous surveillance data.

A conceptual and computational framework for modeling the complex, adaptive dynamics of epidemics: The case of the SARS-CoV-2 pandemic in Mexico.

In the quest to ensure adequate preparedness for health emergencies caused by infectious disease pan...

Leveraging artificial intelligence to promote COVID-19 appropriate behaviour in a healthcare institution from north India: A feasibility study.

Background & Objectives Non-pharmacological interventions (NPI) were crucial in curbing the initial ...

Vaccination hesitancy: agreement between WHO and ChatGPT-4.0 or Gemini Advanced.

BACKGROUND: An increasing number of individuals use online Artificial Intelligence (AI) - based chat...

EACVP: An ESM-2 LM Framework Combined CNN and CBAM Attention to Predict Anti-coronavirus Peptides.

BACKGROUND: The novel coronavirus pneumonia (COVID-19) outbreak in late 2019 killed millions worldwi...

Mitigating epidemic spread in complex networks based on deep reinforcement learning.

Complex networks are susceptible to contagious cascades, underscoring the urgency for effective epid...

An Integrated Approach to Develop a Potent Vaccine Candidate Construct Against Prostate Cancer by Utilizing Machine Learning and Bioinformatics.

BACKGROUND: Prostate cancer is the most common malignancy among males. Prostaglandin G/H synthase (P...

Attention-aware differential learning for predicting peptide-MHC class I binding and T cell receptor recognition.

The identification of neoantigens is crucial for advancing vaccines, diagnostics, and immunotherapie...

Deep learning-based design and experimental validation of a medicine-like human antibody library.

Antibody generation requires the use of one or more time-consuming methods, namely animal immunizati...

Integrating Contact Tracing Data to Enhance Outbreak Phylodynamic Inference: A Deep Learning Approach.

Phylodynamics is central to understanding infectious disease dynamics through the integration of gen...

Machine learning-based individualized survival prediction model for prognosis in osteosarcoma: Data from the SEER database.

Patient outcomes of osteosarcoma vary because of tumor heterogeneity and treatment strategies. This ...

Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders.

The COVID-19 pandemic is marked by the successive emergence of new SARS-CoV-2 variants, lineages, an...

[Progress in application of machine learning in epidemiology].

Population based health data collection and analysis are important in epidemiological research. In r...

Analyzing factors of daily travel distances in Japan during the COVID-19 pandemic.

The global impact of the COVID-19 pandemic is widely recognized as a significant concern, with human...

Ontologies related to livestock for the Global Burden of Animal Diseases programme: a review.

The Global Burden of Animal Diseases (GBADs) programme aims to assess the impact of animal health on...

Machine learning and deep learning tools for the automated capture of cancer surveillance data.

The National Cancer Institute and the Department of Energy strategic partnership applies advanced co...

The role of artificial intelligence-assisted endoscopy surveillance in clinical practice: controversies and future perspectives.

Surveillance of Health Care-Associated Violence Using Natural Language Processing.

BACKGROUND AND OBJECTIVES: Patient and family violent outbursts toward staff, caregivers, or through...

Predicting Diabetes in Canadian Adults Using Machine Learning.

Rising diabetes rates have led to increased healthcare costs and health complications. An estimated ...

Towards Fluorescent-Tag-Less Viral Titration: Automated Estimation of Cell-Size Distribution and Infection Level from Phase-Contrast Microscopy Using Deep Learning and Transfer Learning.

Automated detection of infected insect cells is one of the crucial tasks in the field of recombinant...

ECG-based Daily Activity Recognition Using 1D Convolutional Neural Networks.

This study presents an approach to human activity recognition (HAR) using electrocardiogram (ECG) si...